Artificial IntelliTools

  How understanding stakeholder dynamics and implementing structured communication can save your next AI initiative Picture this: You’re halfway through an ambitious AI project. The tech looks promising, leadership is excited, and your team has worked tirelessly for months. Yet somehow, everything feels… wrong. Budgets are ballooning, deliverables are delayed, and your stakeholders are growing restless. Sound familiar? You’re not alone. From healthcare to financial services, from retail to public sector — AI projects are hitting the same walls. After analyzing dozens of real-world case studies, I’ve discovered a surprising truth: it’s rarely the AI that fails. It’s the humans. The Invisible Break Points While we obsess over algorithms and data quality, the real AI project killers lurk in plain sight. Like a perfectly engineered bridge collapsing because someone forgot to secure the foundation, sophisticated AI projects crumble due to fundamental oversights we’ve seen for decad...

 Listen to the report podcast :  Source: Excerpts from :   The state of AI: How organizations are rewiring to capture value March 12, 2025   | Survey Date of Report: March 2025 (Survey data collected July 16-31, 2024) Key Authors: Alex Singla, Alexander Sukharevsky, Lareina Yee, Michael Chui, Bryce Hall Executive Summary: This McKinsey report analyzes the evolving landscape of AI adoption, particularly focusing on generative AI (gen AI), within organizations. The findings reveal a significant increase in both the use of AI and gen AI across various business functions. While still in the early stages of deployment, organizations are beginning to implement structural and process changes, including redesigning workflows, elevating governance, and mitigating risks, to capture meaningful value from gen AI. Larger companies are leading the way in these organizational shifts. The report highlights the correlation between top-down commitment (especially CEO oversight of A...

  Understanding Survival in the ICU: How Logistic Regression Reveals Key Insights Imagine you're in charge of an Intensive Care Unit (ICU). Every day, critically ill patients are admitted, each presenting unique health challenges. Your team’s goal is straightforward yet monumental: ensure the best possible outcomes for every patient. But how do you objectively understand which factors most impact survival rates? Enter logistic regression—an accessible yet powerful statistical tool that can help you make sense of complex medical data. Photo by Anna Shvets:  What Exactly is Logistic Regression? At its core, logistic regression is a statistical method used when the outcome you're interested in has two possible categories: such as survived versus not survived, or disease versus no disease. Instead of predicting exact values, logistic regression estimates the probability that an event will happen. For example, it can predict the likelihood that a patient admitted to the ICU will...

  Demystifying Linear Regression: A Simple Guide to Predicting Real-World Outcomes By Emmanuel Olimi Kasigazi Have you ever wondered how weather forecasters predict temperatures or how businesses forecast sales? At the heart of these predictions lies a simple yet powerful statistical tool known as linear regression. Linear regression might sound intimidating at first, but it's actually a straightforward method that helps us predict one variable based on another. Think of it as a tool that draws the "best-fit line" through data points, helping us understand trends and predict future values. What Exactly is Linear Regression? In simple terms, linear regression explores the relationship between two variables by fitting a straight line through data points. One variable is considered independent (predictor), and the other is dependent (response). For instance, predicting ice cream sales based on temperature: temperature is your predictor, and ice cream sales are the respo...